What Is Great Question?
Great Question is an all-in-one UX research suite. The defining architectural commitment is consolidation: one platform that combines participant recruitment, study management, research execution across eight-plus methods, AI synthesis, and a centralized insight repository. The headline — “Empower teams to do great research. Fast.” — frames the product as research-ops infrastructure, a hub for research where every study, participant, and insight lives in one place rather than scattered across a stack of separate tools.
That positioning matters for buyers because it sets Great Question apart from pure-play interview platforms. Where User Intuition, Strella, and Outset are built around a single research instrument and optimize every engineering decision for that instrument’s depth, Great Question optimizes for method coverage and workflow centralization. The supported methods read like a UXR toolkit: user interviews in 1:1, collective, and round-robin formats, surveys, prototype testing across Figma, desktop, and mobile, card sorting, tree testing, focus groups, online tasks, and AI-moderated interviews in beta.
The research repository is the connective tissue. Every study feeds a centralized library with AI summaries, so insights from a survey, a tree test, and an interview round all land in the same searchable hub. For a research-ops leader, that repository is the reason to buy: it replaces the manual work of stitching findings together across tools and gives a distributed team one source of truth. The CRM-for-research framing — track every participant, every consent record, every prior study — completes the picture.
The trade-off is breadth over depth. AI-moderated interviewing is one feature among many, and it ships in beta. A buyer evaluating Great Question is buying a research operations layer, not a depth-specialist interview engine. The rest of this review evaluates Great Question across five buyer-care dimensions (speed, cost, depth, scale, insights), then how User Intuition approaches the same dimensions, then security diligence and a decision framework.
How Fast Does Great Question Deliver Results?
Great Question’s speed profile depends entirely on which entry path a team takes, and the two paths diverge sharply. Self-Serve onboarding is genuinely fast — a researcher can sign up, start the 14-day free trial with no credit card, and begin building studies the same day. Enterprise procurement is a different timeline, gated by security review, contract negotiation, and SSO provisioning before the first study runs.
For a Self-Serve team, the time-to-first-study runway looks like this:
- Day one: sign up, start the 14-day trial, no card required
- Day one to two: build a study using interviews, surveys, prototype tests, or observer rooms
- Recruitment: instant for own customers via CRM integration; pay-per-participant turnaround for the 6M+ external panel
- Synthesis: AI summaries generate as responses arrive, feeding the centralized repository
- Cap to plan against: five paid seats and 100 participants on the Self-Serve tier
The Enterprise path adds a procurement runway. Card sorting, tree testing, focus groups, round-robin scheduling, SAML/SSO, audit logs, data governance, and MCP access all sit behind a custom annual contract. A buyer who needs those capabilities should plan for a security review and a sales cycle measured in weeks, not the same-day start the Self-Serve trial offers.
The practical read for a buyer: Great Question is fast to evaluate and fast to start on Self-Serve, but the full multi-method platform — the version that justifies the consolidation narrative — sits behind Enterprise procurement. Speed-to-insight inside a study also depends on method. A survey returns data quickly; an AI-moderated interview round in beta, or a recruited focus group, runs on a longer clock. Teams comparing Great Question against a self-serve depth platform should separate “fast to try” from “fast to run the research that matters.”
What Does Great Question Cost?
Great Question publishes its Self-Serve pricing, which is unusual and useful in a category where most platforms hide their numbers behind a sales call. Self-Serve is $129 per seat per month, or $1,290 per seat per year — two months free when billed annually. The Self-Serve plan is capped at five paid seats plus unlimited observers, and it includes interviews, surveys, prototype testing, observer rooms, AI analysis, the research repository, and basic integrations.
Enterprise is custom annual pricing with a five-seat minimum plus unlimited observers. It adds card sorting, tree testing, focus groups, round-robin scheduling, SAML/SSO, dedicated CSM support, audit logs, data governance, and MCP access for Claude, Cursor, and ChatGPT. There is no published Enterprise number; that tier is quoted by sales.
Recruitment cost depends on the audience. Interviewing a team’s own customers via CRM integration is free — a real differentiator. The external 6M+ panel is pay-per-participant, billed on top of the seat subscription. A 14-day free trial with no credit card lets a team evaluate before paying.
For the full cost-by-frequency math at 1, 5, 10, 20, and 50 studies per year, see the Great Question pricing reference guide.
Want to compare per-interview economics? User Intuition publishes per-study pricing — $200 for a 10-interview study at $20 per audio interview, with three free interviews on signup and no credit card. See the pricing calculator →
How Deep Does Great Question Go in Each Interview?
Great Question’s depth profile is best understood as breadth-of-methods rather than depth-of-conversation. A buyer who asks “how deep does this go?” needs to separate three things: how the moderator behaves, how wide the method coverage runs, and how synthesis handles what comes back.
Moderator behavior — AI moderation is a beta feature, not the core engine. Great Question supports AI-moderated interviews, but the capability ships in beta and is one feature among many on a multi-method platform. That positioning is the most important fact for a buyer evaluating interview depth. Because the AI moderator is not the architectural center of the product, engineering investment is spread across surveys, card sorting, tree testing, prototype testing, and the repository. The AI moderator handles a structured discussion guide and asks follow-ups, but it is not built around systematic, multi-level motivational laddering the way a depth-specialist platform is. For research questions where the deliverable is “why did this customer decide what they decided,” that distinction is the whole evaluation.
Methodology breadth — eight-plus methods under one subscription. This is Great Question’s genuine strength. One platform runs user interviews (1:1, collective, round-robin), surveys, prototype testing across Figma, desktop, and mobile, card sorting, tree testing, focus groups, and online tasks. A research-ops team that needs an information-architecture study, a concept survey, and an interview round in the same quarter gets all of it without procuring three tools. Depth here means range — the platform can answer many shapes of research question, each at a competent rather than specialist level.
Synthesis behavior — AI summaries and a centralized repository. Every study generates AI summaries that feed the centralized research repository. Synthesis is automated enough to remove the manual stitch-together step, and because all methods feed the same hub, a buyer gets cross-method visibility. The depth of synthesis is summary-level — themes, highlights, clips — rather than the ontology-indexed, queryable structure a depth platform builds. For most multi-method teams, summary-level synthesis across a unified repository is exactly the right shape; for teams whose core deliverable is motivational architecture from interviews, it is thinner than a purpose-built interview engine.
The honest read: Great Question goes wide, not deep, inside any single interview.
How Does Great Question Scale to Your Research Volume?
Great Question’s scaling story has three independent axes, and a buyer should evaluate each separately rather than assume one number describes the platform.
Audience scaling — a 6M+ external panel plus BYO via CRM. Great Question pairs a 6M+ B2B and B2C external panel with bring-your-own-audience recruitment through CRM integration — Salesforce, Snowflake, and API connections. The external panel is pay-per-participant; own customers recruited through the CRM are free. For audience scale, this is a strong combination: a team can reach broad B2C samples through the panel and run targeted B2B research against its own customer base without per-participant cost. The constraint to plan against is the Self-Serve cap of 100 participants per study; larger audiences move the buyer to Enterprise.
Frequency scaling — a per-seat subscription that scales with team headcount, not study count. Great Question’s commercial model scales with how many researchers a team employs, not with how often it runs research. A five-person team on Self-Serve pays the same whether it runs two studies a year or twenty. That makes the platform economical for high-frequency research-ops teams — the seat cost amortizes across every study — and less economical for occasional researchers who would pay a full seat to run a handful of studies. The five-paid-seat ceiling on Self-Serve is the structural limit; teams that need more researchers in the platform move to Enterprise’s five-seat minimum with no upper cap.
Team scaling — research-ops infrastructure built for distributed teams. This is where the research-ops positioning earns its keep. Unlimited observer seats let stakeholders watch studies without consuming a paid license. Enterprise governance — SAML/SSO, audit logs, data governance controls, role-based access — supports a research practice distributed across a large organization. The centralized repository gives a distributed team one shared source of truth. For an enterprise research function spread across product, design, and marketing, Great Question scales as an organizational layer, not just a tool a few researchers log into.
How Useful Are Great Question’s Insights — and Do They Compound?
Insight usefulness has two parts: how good a single study’s output is, and whether insights accumulate into something more valuable than the sum of the studies.
Per-project insight quality. Within a single study, Great Question delivers competent, method-appropriate output. A survey returns clean quantitative results; a card sort produces a similarity matrix; an interview round generates transcripts, AI summaries, and shareable highlight clips. The AI synthesis layer removes the manual work of pulling themes together, and observer rooms let stakeholders see the research as it happens, which raises the odds the insight actually gets used. The ceiling on per-project quality is set by the methods themselves: because AI-moderated interviewing is a beta feature rather than the core engine, the motivational depth of any single interview is shallower than a depth-specialist platform produces. For most multi-method UXR work, the quality is appropriate to the question being asked.
Insight compounding via the centralized repository. This is a real Great Question strength and a buyer should weight it accordingly. Great Question has a centralized research repository with AI summaries, so insights genuinely do compound — every study feeds a searchable library, and a researcher six months later can find what a prior study learned without re-running it. That repository is a meaningful asset and one of the strongest reasons research-ops leaders buy the platform. The honest distinction is the retrieval model. Great Question’s repository is built on search — a researcher queries by keyword, tag, or study, and the platform returns matching documents and clips.
Consider a concrete cross-study question: “Across every study we ran this year, what made enterprise buyers hesitate before purchase?” In Great Question, a researcher searches the repository for “enterprise,” “hesitation,” “purchase,” reads the matching clips and summaries, and synthesizes the answer manually. In User Intuition’s Customer Intelligence Hub, the same plain-language question is an ontology-indexed query that returns a synthesized answer across every past study — because each interview was indexed against a structured ontology of behaviors, motivations, and identity markers at capture time. Both platforms compound insight; Great Question compounds it as a searchable library, User Intuition compounds it as a queryable knowledge structure.
How Does User Intuition Approach the Same Dimensions?
User Intuition and Great Question are not the same product evaluated at different price points — they are different lanes. Great Question is breadth-of-methods plus a research-ops repository, sold per seat to teams consolidating tool sprawl. User Intuition is depth-of-conversation: an audio-first platform built around adaptive 5-7 level laddering on every interview, sold per interview to teams whose deliverable is motivational understanding. The five dimensions below contrast the two so a buyer can see where each one wins.
Speed
Where Great Question’s speed splits into a fast Self-Serve trial and a slower Enterprise procurement runway, User Intuition runs one path: sign up, get three free interviews with no credit card, and launch a study in roughly five minutes. Themed results land in 24-48 hours because the 4M+ vetted panel is ready at signup — no panel-partner negotiation, no pay-per-participant clock to start. Speed-to-insight is a single number, not a function of which method or which tier.
Cost
Where Great Question’s per-seat subscription bills by team headcount — $129 per seat per month, five-seat Self-Serve cap, Enterprise quoted by sales — User Intuition bills per interview: $20 per audio interview, $200 for a 10-interview study, no seat licenses and no annual contract. The economic model scales with how often a team runs research, not how many researchers it employs. A buyer running occasional research pays only for the studies it runs.
Depth
Where Great Question’s interview depth is bounded by AI moderation being a beta feature on a multi-method platform, User Intuition’s entire architecture is the interview. Every conversation runs adaptive 5-7 level laddering — moving systematically from a stated behavior down through functional reasons to emotional drivers and identity markers. Depth is the product, not a feature competing for engineering attention with surveys and tree tests.
Scale
Where Great Question scales as a research-ops layer across a distributed organization, User Intuition scales as research throughput: a 4M+ vetted panel across 50+ languages, with the same per-interview economics whether a team runs one study or fifty. Both scale well; they scale different things — Great Question scales the team, User Intuition scales the volume of conversations.
Insights
Where Great Question compounds insight as a searchable repository of summaries and clips, User Intuition compounds it as the Customer Intelligence Hub — an ontology-indexed knowledge structure that answers plain-language questions across every past study. Both make a year of research more valuable than its individual studies; the retrieval model differs between library search and structured query.
Side-by-side at a glance
| Dimension | Great Question | User Intuition |
|---|---|---|
| Core architecture | All-in-one UXR suite + research-ops repository | Audio-first depth-specialist interview engine |
| Research methods | Interviews, surveys, card sort, tree test, prototype, focus group, online tasks | Adaptive AI-moderated interviews |
| Interview depth | Structured guide; AI moderation in beta | Adaptive 5-7 level laddering on every interview |
| Pricing model | Per seat — $129/mo or $1,290/yr Self-Serve | Per interview — $20 audio, $200 per 10-interview study |
| Free evaluation | 14-day trial, no card | Three free interviews, no card |
| Panel | 6M+ external panel + BYO via CRM | 4M+ vetted panel + BYO via CRM |
| Languages | Not disclosed on the live site | 50+ languages |
| Time to results | Self-Serve fast; Enterprise procurement runway | 24-48 hours end-to-end |
| Insight model | Centralized repository, search retrieval | Customer Intelligence Hub, ontology query |
| Security posture | SOC 2 Type II + HIPAA + GDPR today | GDPR-compliant; SOC 2 audit in progress |
How Do Great Question and User Intuition Compare on Security and Compliance Posture?
Security is the dimension where Great Question is genuinely ahead today, and a buyer should weigh that honestly rather than discount it.
| Posture element | Great Question | User Intuition |
|---|---|---|
| SOC 2 Type II | Certified today | Audit in progress |
| HIPAA | Compliant today | HIPAA-aligned; supports HIPAA workflows |
| GDPR | Compliant | Compliant |
| Penetration testing | Regular third-party testing | Regular third-party testing |
| Enterprise governance | SAML/SSO, audit logs, data governance, RBAC (Enterprise tier) | Available; review with sales for current state |
Great Question carries SOC 2 Type II certification, HIPAA compliance, and GDPR compliance today, backed by regular third-party penetration testing and, on the Enterprise tier, SAML/SSO, audit logs, data governance controls, and role-based access. User Intuition is GDPR-compliant and supports HIPAA workflows, with its SOC 2 audit in progress rather than complete.
The honest framing for a buyer: if a completed SOC 2 Type II report and HIPAA compliance are a hard procurement gate today — and for regulated-industry buyers, healthcare research, or enterprise vendor reviews they often are — Great Question clears that gate now and User Intuition does not yet. This is one of the clearest competitor advantages on security in any platform comparison, and it should not be softened. The right question for a buyer is timing: if the security review is happening this quarter and the report is mandatory, that is a decision in Great Question’s favor. If the SOC 2 timeline allows for an audit completing during the evaluation window, the gap narrows. Either way, ask both vendors for current attestation status and let procurement set the threshold.
For details on sub-processor disclosure and current certification status, see the User Intuition security page.
How to Choose Between Great Question and User Intuition
The choice resolves cleanly along three axes. Match a team’s situation against the tables below.
By research-question type:
| Research question | Better fit |
|---|---|
| Information architecture, navigation, concept feedback | Great Question |
| Why customers churn, decide, or resist | User Intuition |
| Mixed-method study spanning surveys + interviews + tests | Great Question |
By research-team size:
| Team shape | Better fit |
|---|---|
| Dedicated research-ops function, 3+ researchers | Great Question |
| Individual researcher or small embedded team | User Intuition |
| Distributed org needing a shared repository + governance | Great Question |
By operating model:
| Operating model | Better fit |
|---|---|
| Consolidating a stack of separate research tools | Great Question |
| Per-study budgeting, no seat subscriptions | User Intuition |
| SOC 2 Type II + HIPAA required today | Great Question |
Two-platform answer. These platforms are not mutually exclusive, and some organizations run both deliberately. Great Question serves as the research-ops layer and multi-method workhorse — the place a team runs surveys, card sorts, tree tests, and prototype tests, and the repository where all of it accumulates. User Intuition serves as the depth-specialist engine for the motivational studies that drive positioning, retention, and product strategy. A research-ops leader can keep Great Question as the team’s operational hub while routing the “why” questions to User Intuition’s adaptive laddering, then decide later whether to consolidate. Running both for a quarter is often the fastest way to see which lane each platform actually wins.
Evaluation Questions for Your Great Question Demo
Bring these questions to a Great Question demo so the evaluation tests the dimensions that matter for a specific team rather than the dimensions the sales narrative leads with.
On the AI-moderated interview feature
- AI-moderated interviewing is in beta — what is the roadmap to general availability, and what changes between beta and GA?
- Inside an AI-moderated interview, how does the moderator decide when to ask a follow-up versus move on, and how many levels deep can it probe?
- For a motivational research question — why a customer churned — how does the AI moderator’s output compare to a depth-specialist interview platform?
On pricing and seats 4. Self-Serve caps at five paid seats — what happens to our cost structure the day we need a sixth researcher? 5. For external panel recruitment, what is the per-participant cost, and how is it billed against the seat subscription? 6. What does the Enterprise annual contract actually cost for our team size, and what is the minimum commitment?
On the repository and methods 7. How does repository search work across studies — keyword, tag, semantic — and can it answer a cross-study question without manual synthesis? 8. Which methods are Self-Serve and which are Enterprise-only, and where does our planned research land? 9. How does MCP access work, which tools does it connect to, and is it Enterprise-only?
On security and procurement 10. Can we see the current SOC 2 Type II report and HIPAA documentation before signing? 11. What is the typical procurement runway from first demo to first study on Enterprise? 12. How are audit logs, SSO, and data governance provisioned, and on which tier? 13. What is exportable if we leave — repository data, study results, participant lists — and in what format?
Three free interviews. No card. 5 minutes to launch. 5/5 on G2 and Capterra. Try User Intuition → · Compare Great Question vs User Intuition → · Great Question pricing reference → · 7 Great Question alternatives compared → · Migration guide →